Our work makes two main contributions. First, we develop a multi-label DL system that can automatically classify the BI-RADS density of mammograms. To our knowledge, we show for the first time in this work that a DL model trained on a large-scale, annotated dataset can predict both BI-RADS and density scores accurately at the same time. Second, the proposed approach is based on a novel two-stage multi-view classifier. The first stage learns feature extractors for each view individually. At the second stage, learned features are fused and afterward trained by a LightGBM classifier [1]} to predict BI-RADS and density scores. Experimental results show that our multi-view approach outperforms the single-view model up to 5\(\%\)  in terms of F1-score. External evaluation on the DDSM dataset [2]} also demonstrates the effectiveness of the proposed approach with an increase of 10\(\%\)  in pathology classification compared to the single view approach. The rest of the paper is organized as follows. The proposed approach is presented in Section . The experiments are provided in Section . Finally, Section  discusses the experimental results and concludes the work as well as presents its perspectives.
